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• A new hybrid prediction system is proposed to predict wind speed. • Simplex and chaotic mapping optimization cuckoo algorithm are proposed. • An uncertainty prediction model based on Monte Carlo method is proposed. • The energy theory is proposed to optimize the decomposition mode number. • Theories proofs and experiments verify the effectiveness of the model. Wind energy has strong volatility and intermittent. Accurate wind speed prediction can not only improve the safety of the system, but also optimize dispatch and reduce economic losses. However, previous studies tend to ignore the influence of virtual components and lack effective identification of wind speed characteristics and a robust interval prediction scheme, resulting in poor results. To bridge these gaps, this paper proposes an energy theory method to solve the problem of modal over-decomposition. The study also combines effective modal recognition, uses different prediction methods according to modal characteristics and proposes a set of new optimization algorithms to improve nonlinear prediction capabilities. Finally, based on Monte Carlo theory, a set of interval prediction schemes that can adapt to different error characteristics are proposed. Under the verification of wind speed data in Changma, China and Sotavento, Spain. The mean absolute percentage error of wind speed deterministic prediction reaches 4.22% and 5.82%, respectively. The coverage rate of wind speed uncertainty prediction meets different confidence requirements, and the average interval width is still less than 2.5 m/s at 90% confidence. The results show that the forecasting system proposed in this paper is significantly better than all the comparative forecasting schemes, which can reduce the risk of fluctuations and improve the stability and safety of the wind power system. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an evolutionary algorithm for wind speed reconstruction from synoptic pressure patterns. The algorithm operates in a search space formed by grids of pressure measures, and must classify the different situations into classes, in such a way that a measure of wind speed in a given point is minimized among patterns assigned to the same class. Then, each class is assigned a mean wind speed and direction, so the wind speed reconstruction is possible for a new grid of synoptic pressures. In this paper we present the problem model and the specific description of the evolutionary algorithm proposed to solve the problem. We also show the good performance of the proposed method in the reconstruction of the average wind speed in six wind towers in Spain. The proposed method is applicable to wind speed reconstruction or reconstruction of wind missing data of wind series, specially when there is no other variable or related measure available. [Copyright &y& Elsevier]

Highlights • Hourly-resolved model to investigate highly-renewable electricity generation in Spain. • Correlation analysis of time series to create 900 combinations used in simulations. • Transition paths evaluated based on security of supply, CO 2 emissions, and renewable share. • Short-term phase-out of nuclear and coal power plants proven to be feasible. Abstract Two alternative paths to achieve highly-renewable electricity generation in peninsular Spain are investigated in this paper. Every transition path comprises a description of the installed and decommissioned generation and storage capacities, from 2017 to 2030, as well as a hypothesis on the evolution of the electricity demand. The electricity mix for every hour within the transition path is determined through a dispatch algorithm that prioritizes electricity from renewable energy sources. The simulation is run for 900 different combinations of time series representing the hourly capacity factors of different technologies, as well as the electricity demand. This robust approach allows the evaluation of the transition paths based on the statistical distribution of several defined assessment criteria, such as security of supply, CO 2 emissions or renewable share in electricity generation. The feasibility of a Spanish power system with high renewable penetration is investigated not only in a future reference year but throughout the transition path. In particular, a progressive and simultaneous phase-out of nuclear and coal power plants in the short-term is proven to be feasible. Furthermore, the results sensitivity is analyzed including scenarios with a delayed nuclear phase-out, lower hydroelectricity generation due to more frequent and severe droughts caused by climate change and higher annual increment for the electricity demand. [ABSTRACT FROM AUTHOR]

In this paper, we explore two strategies for reducing the cash flow uncertainty of wind energy producers ascribed to variable weather conditions. The first strategy is based on the idea of aggregating the output of geographically-dispersed generating units. The second strategy employs financial instruments that compensate producers for unanticipated declines in the power delivery. Using a blend of advanced weather modeling, time series analysis, simulation and optimization techniques, we empirically access the ability of the two risk management approaches to control volumetric risk in Spain. With the aid of factor analysis techniques, we proceed with the decomposition of the remaining risk levels and assess the exposure of each strategy's revenue to systematic risk factors. Motivated by the results of this analysis, we propose new financial contracts and mixed-style strategies that are better suited to the risk position of each market player. • We introduce various strategies for managing the wind volumetric risk in Spain. • We quantify their ability to reduce the revenue variance of wind power producers. • Spatial aggregation of resources is not a cure for wind energy stochasticity. • Commercial wind power futures cannot hedge away all risk dimensions. • New contract designs are needed to control systematic variations in producers' income. [ABSTRACT FROM AUTHOR]

In this paper, a case study of performing a reliable statistical procedure to evaluate the quality of HVAC systems in buildings using data retrieved from an ad hoc big data web energy platform is presented. The proposed methodology based on statistical quality control (SQC) is used to analyze the real state of thermal comfort and energy efficiency of the offices of the company FRIDAMA (Spain) in a reliable way. Non-conformities or alarms, and the actual assignable causes of these out of control states are detected. The capability to meet specification requirements is also analyzed. Tools and packages implemented in the open-source R software are employed to apply the different procedures. First, this study proposes to fit ARIMA time series models to CTQ variables. Then, the application of Shewhart and EWMA control charts to the time series residuals is proposed to control and monitor thermal comfort and energy consumption in buildings. Once thermal comfort and consumption variability are estimated, the implementation of capability indexes for autocorrelated variables is proposed to calculate the degree to which standards specifications are met. According with case study results, the proposed methodology has detected real anomalies in HVAC installation, helping to detect assignable causes and to make appropriate decisions. One of the goals is to perform and describe step by step this statistical procedure in order to be replicated by practitioners in a better way. [ABSTRACT FROM AUTHOR]

Parabolic-trough solar collector fields are large-scale systems, so the application of centralized optimization-based control methods to these systems is often not suitable for real-time control. As such, this paper formulates a novel coalitional control approach as an appropriate alternative to the centralized scheme. The key idea is to split the overall solar collector field into smaller subsystems, each of them governed by a local controller. Then, controllers are clustered into coalitions to solve a local optimization-based problem related to the corresponding subset of subsystems, so that an approximate solution of the original centralized problem can be obtained in a decentralized fashion. However, the operational constraints of the solar collector field couple the optimization problems of the multiple coalitions, thus limiting the ability to solve them in a fully decentralized manner. To overcome this issue, a novel population-dynamics-assisted resource allocation strategy is proposed as a mechanism to decouple the local optimization problems of the multiple coalitions. The proposed coalitional methodology allows to solve the multiple local subproblems in parallel, hence reducing the overall computational burden, while guaranteeing the satisfaction of the operational constraints and without significantly compromising the overall performance. The effectiveness of proposed approach is shown through numerical simulations of a 10- and 100-loop version of the ACUREX solar collector field of Plataforma Solar de Almería , Spain. • Decentralized control of large-scale parabolic-trough solar collector fields. • Reducing the computational burden of model predictive controllers for solar plants. • Splitting large-scale solar plants by clustering model predictive controllers. • Improving the scalability of model predictive control for large-scale solar plants. • Evolutionary game theoretical assistance for model predictive control in solar plants. [ABSTRACT FROM AUTHOR]

Costs for High Concentrator Photovoltaic (HCPV) power plants dropped dramatically during 2013 and are expected to continue to fall in the next few years. Moreover, when viewed from the perspective of life cycle cost, HCPV becomes even more competitive than other renewable technologies in some geographical areas. Consequently, an analysis of the economic feasibility of the HCPV systems in future scenarios is necessary for comparison with other electricity generation technologies. One of the methods commonly used for the economic feasibility analysis in electricity generation projects is the levelised cost of electricity (LCOE). In this paper a cost analysis of electricity generation of HCPV technology in Spain by using the LCOE has been carried out. The results obtained in this LCOE analysis show that in 2020 the LCOE will be able to reach values for HCPV systems from 0.035 to 0.080 €/kW h, lower than LCOE for conventional PV systems, in some geographical areas of Spain. The results obtained in this analysis have been shown in innovative maps. [ABSTRACT FROM AUTHOR]

Distinct responses of electricity demand to temperature changes are usually assumed for residential, industrial, and service sectors. However, available empirical studies on the temperature effect of electricity demand provide only partial evidence or evidence at a very aggregate level. Our paper aims to partially fill this existing gap in the literature. Using disaggregated data by sectors, we carry out a comprehensive analysis of the sectorial breakdown of temperature effects on firms’ electricity demand. In-depth knowledge of sectorial demand responses to temperature changes is fundamental for improved energy planning because such responses represent a potential source of demand price inelasticity, one of the main obstacles for developing adequate demand response planning mechanisms. Our findings indicate that in Spain firms’ aggregate electricity demand is rather insensitive to temperature. However, there are marked differences among sectors, with the highest sensitivity found for firms in the service sector. We show that the aggregate insensitiveness of firms’ electricity demand to temperature is mainly due to a demand composition effect. [ABSTRACT FROM AUTHOR]

The Spanish Integral Plan of Industrial Policy 2020 points out the energy cost containment as one of the priority actions to improve the competitiveness of industry. In fact, electricity prices for industrial consumers are of particular importance for international competitiveness, as electricity usually represents a significant proportion of its total energy costs. In order to establish economic policies to reduce industrial electricity prices, it is important to analyze its determinants. Mainly, since the liberalization of the Spanish electricity industry, a wholesale electricity market is created where electricity price is determined by the short-term marginal cost (mainly the fuels) of the different electricity generation technologies. This paper analyses how important are fuel cost- such as natural gas, coal and oil prices- in explaining the variations in Spanish electricity prices for industrial consumers by using a General Maximum Entropy Econometric approach. The obtained results suggest that imported fuel prices could affect competitiveness of manufacturing and other industries as it involves important effects on the level and stability of the electricity prices. The promotion of clean technologies, the increase of the energy and resource efficiency of Spanish companies or the implementation of energy conservation projects at industrial sector become a way to reduce the vulnerability of Spanish industry competitiveness to electricity prices. [ABSTRACT FROM AUTHOR]

Abstract: A wind clustering methodology capable of dynamically characterizing and long-term reconstructing daily surface wind series is introduced and tested for six meteorological towers at different wind farms in Spain, for the period 1871–2009. On this basis this paper provides for the first time a centennial surface wind reconstruction with a daily resolution without the need of numerical simulations. Thus, several soft-computing algorithms are developed, with public domain Sea Level Pressure (SLP) Reanalysis data as the only input. These algorithms are constructed by tackling an Euclidean distances’ problem at the geostrophic speeds’ space. Once the wind-independent classifications are obtained, the methodology is calibrated by linking the obtained classifications with observed wind data, thus allowing to estimate and characterize the daily surface wind speed and direction. A cross-validation is then performed in order to obtain several measures of goodness of the method, such as its wind speed estimation uncertainty in terms of Mean Absolute Error (MAE) and Pearson correlation (r) for both the wind module and vectorial values. Regarding previous approaches, this statistic downscaling shows an outstanding performance: Wind speed module estimates produce a MAE of 1.12m/s (0.32m/s) in some towers for a daily (monthly) scale, as r reaches values of 0.78 (daily scale) and 0.91 (monthly scale). The wind-independent classifications allowed to perform daily surface wind speed and rose reconstructions in time periods when no wind data are available, which constitutes the main goal of this work. Thus, a 140 year daily wind reconstruction is performed and analyzed for one tower located at central Iberia. There, significant low frequency variations are detected, as well as wind speed oscillations in the 20y band. Remarkable changes are also identified over reconstructed decadal wind speed frequency distributions and wind rose. Since long-term wind measurements are rarely available at modern wind farm sites, such an analysis on centennial reconstructed wind series can represent an appropriate tool that places the last years of observed wind speed in a climatological perspective. [Copyright &y& Elsevier]

Abstract: Climate change, added to security of supply concerns, has been leading many countries to strongly support the development of electric vehicles (EVs) not only as a cleaner and more energy efficient source of transportation but also as a flexible electricity storage option. However, the massive deployment of EVs will impose important challenges to power systems operation. In this context, this paper investigates the impact of EVs with V2G capability to power system operation. Since this impact depends on the power system considered and on the number (and type) of EVs, different scenarios for renewable energy sources (RES) generation and EV penetration are studied. A detailed analysis of the impact of EVs the Spanish power system – demand, generation, use of flexible units (pumped storage) and power reserve, and energy curtailment – is performed. For this purpose, a mid-term operation model that computes generation dispatch and simulates hourly power system operation is used. The advantage of this model is that it includes EVs in the unit commitment and represents in detail mobility and connection patterns, which allows the estimation of more realistic results. [Copyright &y& Elsevier]

Abstract: Life Cycle Assessment (LCA) has been conducted for seven experimental cubicles located in Puigverd de Lleida (Spain). The objective of this experimental set-up is to test different constructive solutions in order to point out the most sustainable solution with lower energy demand during the operational phase. Therefore, different building, insulation and Phase Change Materials (PCMs) have been tested under controlled temperature conditions to examine the thermal performance of the whole system. Although some of these materials are able to reduce the energy demand and consequently the environmental impact during the operational phase, they still have high embodied energy that can cause high environmental impact during the manufacturing phase. Therefore the LCA study in this paper focuses on assessing the impact of the embodied energy needed during the manufacturing and disposal phase by highlighting and comparing the effect of using different building materials, insulating materials, and phase change materials. [Copyright &y& Elsevier]

Abstract: Demand response has proven to be a useful mechanism that produces important benefits for both the customer and the power system. In the context of an increasingly competitive electricity market, where prices are constantly rising and the presence of renewable energy resources is gaining prominence, this paper analyzes the flexibility potential of customers in the meat industry, based on the management of the most energy consuming process in this type of segment: cooling production and distribution. The effectiveness of the proposed actions has been successfully tested and validated in an active factory that produces cured ham in Spain, where savings of about 5% in the total annual cost of electricity have been assessed, together with power reductions in the range of 50% of the total peak demand of the studied facilities. Such results demonstrate the efficacy of these techniques, and they open the door to an innovative perspective on the evaluation of flexibility among customers which are traditionally considered rigid, providing a novel approach to the management of customer infrastructures in order to exploit their flexibility in electricity markets. [Copyright &y& Elsevier]

Abstract: Competitive structure of power markets causes various challenges for wind resources to participate in these markets. Indeed, production uncertainty is the main cause of their low income. Thus, they are usually supported by system operators, which is in contrast with the competitive paradigm of power markets. In this paper, a new strategy for increasing the profits of wind resources is proposed. In the suggested strategy, a Generation Company (GenCo), who owns both wind and pumped-storage plants, self-schedules the integrated operation of them regarding the uncertainty of wind power generation. For presenting an integrated self-schedule and obtaining a real added value of the strategy, participation of the GenCo in energy and ancillary service markets is modeled. The self-scheduling strategy is based on stochastic programming techniques. Outputs of the problem include generation offers in day-ahead energy market and ancillary service markets, including spinning and regulation reserve markets. A Neural Network (NN) based technique is used for modeling the uncertainty of wind power production. The proposed strategy is tested on a real wind farm in mainland, Spain. Moreover, added value of the strategy is presented in different conditions of the market. [Copyright &y& Elsevier]

Abstract: This paper describes an application of the Strength Pareto Evolutionary Algorithm to the multi-objective optimization of a stand-alone PV–wind–diesel system with batteries storage. The objectives to be minimized are the levelized cost of energy (LCOE) and the equivalent carbon dioxide (CO2) life cycle emissions (LCE). Each solution of the Pareto front is a possible solution for the PV–wind–diesel-batteries system, which can supply the load, but each one has a different LCOE and LCE. Some solutions have low LCOE values, but high LCE values, and vice versa. Results show that the photovoltaic (PV) generator is the most important source of electrical energy for stand-alone systems in Spain and Southern Europe, not only environmentally, but also economically. In some cases, PV is almost the only source of energy, as some solutions of the best Pareto front do not include wind turbines and diesel generators run only a few hours during the year. [Copyright &y& Elsevier]

Abstract: Assessing the correct operation of PV grid-connected systems (PVGCS) is paramount not only for mere energy and profitability concerns but also for safety reasons. Intended to this purpose, the IEC 62446 standard is open to some tests to be required in some circumstances, in addition to those stated in this document. Our work proposes a procedure to carry out quality checks in PVGCS complying with this standard and dealing with some tests not mentioned in it – namely, PV generator peak power measurement, inverter response and earth electrode measurement – so that the verification of the system gets more comprehensive. This procedure was carried out in six PV plants sited in different locations with a Mediterranean climate in Spain. The most remarkable results of this experimental campaign may be summarized as follows: an excellent on-site behavior of the inverters, in general very good figures for the isolation and earth electrode resistance and a scarcely relevant existence of hot spots. On the down side, some figures for the peak power of the tested PV generators well below their nominal value were found. The procedure described in this paper has proved to be a useful tool to assist in the verification of a PVGCS after installation and for subsequent re-inspection or maintenance. [Copyright &y& Elsevier]

Abstract: This paper presents the analysis of the performance of a solar cooling facility along one summer season using a commercial single-effect water–lithium bromide absorption chiller aiming at domestic applications. The facility works only with solar energy using flat plate collectors and it is located at Universidad Carlos III de Madrid, Spain. The statistical analysis performed with the gathered data shows the influence of five daily operational variables on the system performance. These variables are solar energy received along the day (H) and the average values, along the operating period of the solar cooling facility (from sunrise to the end of the cold-water production), of the ambient temperature (), the wind velocity magnitude (V), the wind direction (θ) and the relative humidity (RH). First order correlation functions are given. The analysis of the data allows concluding that the most influential variables on the daily cooling energy produced and the daily averaged solar COP are H, V and θ. The period length of cold-water production is determined mainly by H and . [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a novel price forecasting method based on wavelet transform combined with ARIMA and GARCH models. By wavelet transform, the historical price series is decomposed and reconstructed into one approximation series and some detail series. Then each subseries can be separately predicted by a suitable time series model. The final forecast is obtained by composing the forecasted results of each subseries. This proposed method is examined on Spanish and PJM electricity markets and compared with some other forecasting methods. [ABSTRACT FROM AUTHOR]

Abstract: Spain is a country with six LNG terminals in operation and three more scheduled for 2011. At the same time an increasing number of LNG tanks are under construction to compensate the Spanish lack of underground storage. A method for evaluating the daily boil off generated is presented in this paper. This method is applied to evaluate the increase of BOG to be handle by LNG terminals in 2016, studying the best commercially available solution to be installed. Finally, as a solution to tackle with the BOG a cogeneration plant is suggested. This option will reduce terminal’s operational costs increasing its availability. [ABSTRACT FROM AUTHOR]

Abstract: Life Cycle Assessment (LCA) has been applied within the residential building sector of two buildings, one in each a developed (Spain) and a developing (Colombia) country. The main goal of this paper involves the environmental loads and also brings together the operational energy for activities during the operation phase such as HVAC, domestic hot water, electrical appliances, cooking and illumination. The present research compares two real scenarios: Situation 1, where 100% of the dwelling’s energy is supplied with electricity only and Situation 2, where dwellings can be operated with natural gas plus electricity. The results for the environmental impacts using natural gas plus electricity show that of the Spanish environmental impacts air conditioning had the highest impact with approximately 27–42% due to the electricity used to power it. In Colombian results showed that electrical appliances had the highest environmental impacts in the same order of magnitude with approximately 60% and cooking had the best reduction of emissions due to the use of natural gas, from 10% down to less than 2%. The origin of the energy source used in each Country plays an important role to minimize environmental impacts, as was demonstrated by the environmental impacts of its use in Colombia where 78% of the electricity came from hydroelectric plants whereas in Spain it is more mixed, fossil fuels represented 55%, nuclear 18% and wind 9%. In summary, LCA has been applied because this methodology supports the decision making to concern environmental sustainability. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to carry out a comparative study with regard to energy consumption and greenhouse gas emissions, in respect of two types of engines with three different fuels. The fuels analysed are diesel, biodiesel 30% (B30) and compressed natural gas (CNG). The engines tested were a spark ignition engine (Otto cycle) and two compression ignition engines (Diesel cycle), the first fed with CNG and the last two with B30 and diesel. What is new about this study is its scope of application concerning refuse collection services in the city of Madrid. The tests were carried out on refuse trucks of the FCC Company along actual urban routes in the city of Madrid. Also taken into account were the energy input and the greenhouse gases emitted for each of the paths taken by the fuels analysed, from resource recovery to delivery to the vehicle tank. [Copyright &y& Elsevier]

• A MILP is set to ex-post optimise the Interruptible Load program in Spain. • Consideration of real technical and regulatory constraints. • Definition of a series of metrics to compare the optimal performance of programs. • Recommendation to design and operate Interruptible Load programs. • The optimal usage of the resource could have saved the system a total of 163 M€. The deployment of demand response in reserve markets has been widely discussed. Interruptible Load programs contract demand capacity from consumers in exchange for fixed and variable payments. System operators contract these resources to increase system resilience and use them for economic purposes. Although common, no ex-post evaluations of the program's performance and efficiency exists in the literature. To fill this gap, the paper presents a procedure to evaluate the optimal usage of these resources from a system operator perspective. A Mixed Integer Linear Problem is set to minimise the overall cost of the system using the participant demand resources in the tertiary reserve market, while ensuring that all technical and regulatory constraints are fulfilled in the evaluation. The proposed method describes a series of metrics to compare the optimal performance with the current scenario, and we draw a set of conclusions and policy recommendations from it. We apply the method to the Spanish Interruptible Load program. Our results show that during the five and a half years of the program, demand resources could have provided savings to the system of up to 23% of the cost of tertiary reserve. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel genetic algorithm-based seasonal fractional grey model is proposed. • Monthly electricity generation is forecasted since the Covid-19 lockdown started. • Seasonal grey and machine learning models are used for forecasting. • The selected countries are France, Germany, Spain, Turkey, and the UK. • The share of renewables in total electricity generation is forecasted. Balances in the energy sector have changed since the implementation of the Covid-19 pandemic lockdown in Europe. This paper analyses how the lockdown affected electricity generation in European countries and how it will reshape future energy generation. Monthly electricity generation from total renewables and non-renewables in France, Germany, Spain, Turkey, and the UK from January 2017 to September 2020 were evaluated and compared. Four seasonal grey prediction models and three machine learning methods were used for forecasting; the quarterly results are presented to the end of 2021. Additionally, the share of electricity generation from renewables in total electricity generation from 2017 to 2021 for the selected countries was compared. Electricity generation from total non-renewables in the second quarter of 2020 for France, Germany, Spain, and the UK decreased by 21%–25% compared to the same period of 2019; the decline in Turkey was approximately 11%. Additionally, electricity generation from non-renewables in the third quarter of 2020 for all countries, except Turkey, decreased compared to the same period of the previous year. All grey prediction models and support vector machine method forecast that the share of renewables in total electricity generation will increase continuously in France, Germany, Spain, and the UK to the end of 2021. The forecasting methods provided by this study open new avenues for research on the impact of the Covid-19 pandemic on the future of the energy sector. [ABSTRACT FROM AUTHOR]

Given the climate change emergency, reducing energy consumption, which is responsible for most greenhouse gases emissions worldwide, is a priority. However, the strong historical link between energy consumption and economic growth questions whether continued economic growth is compatible with energy conservation targets. Conventional final energy analysis (common analysis methods applied at the final energy stage) has provided limited insights to this nexus. In response, this paper explores the extent to which useful stage analysis provides additional insights using three common methods: aggregate energy-economy analysis (growth rates, energy intensities, and Index Decomposition Analysis), energy-GDP causality testing, and Aggregate Production Function modelling, using Spain (1960–2016) as empirical case study. The results reveal that of the three methods investigated, aggregate energy-economy analysis provides the greatest insights, including that Spain is far from achieving absolute energy-GDP decoupling. Further, moving to the useful stage indicates that the extent of decoupling is even less than suggested at the final energy stage, and that increasing final energy consumption has historically fully offset efficiency gains. In contrast, whether applied at the final energy or useful stage, energy-GDP causality testing and Aggregate Production Function modelling reveal little about the energy-economy nexus — the results even suggest that these tools are not appropriate and may mislead. Thus, useful stage analysis is necessary but not sufficient for delivering further energy-economy insights; there is also a need for exploring alternative, reliable, energy-economy analysis methods. Indeed, the lack of robustness of Aggregate Production Function modelling and energy-GDP causality testing is worrisome. • Final energy analysis has delivered limited insights to the energy-economy nexus. • Three common analysis methods are assessed at both final and useful energy stages. • Aggregate energy-economy analysis yields additional benefits at the useful stage. • Energy-GDP causality testing and Aggregate Production Functions lack insights at both energy stages. • A move to alternative, useful stage energy-economy analytical methods is required. [ABSTRACT FROM AUTHOR]

• Assessment of responsiveness' role in mechanisms for adjusting economic incentives. • Agent-based model of feed-in tariff for solar adoption in Germany, Spain, Switzerland. • More responsive designs meet deployment policy goal accurately and lower uncertainty. • Surprisingly, results show diminishing returns of responsiveness that add policy cost. • Country-specific attributes shown to influence solar power diffusion patterns. A rapid, global diffusion of clean energy technologies is central to the efforts to curb global carbon dioxide emissions. Deployment policies based on economic incentives have proved effective in accelerating the uptake of renewable technologies, such as solar photovoltaics, and could quicken the adoption of others. However, their outcomes were often uncertain and costly as many countries struggled to adjust incentives while technology prices fell rapidly. This study addresses this challenge by investigating the role of responsiveness – the policy's ability to react to changes in the context in which it operates – in the design of mechanisms for adjusting incentives over time. This paper employs an agent-based model to evaluate quantitatively six policy designs with varying degrees of responsiveness for adjusting a feed-in tariff for solar photovoltaics in three countries – Germany, Spain, and Switzerland – for over a decade. Our first finding confirms that more responsive policy designs tended to produce policies that meet their goals more accurately and certainly. Between the least and the most responsive designs, deviation from policy goals was reduced by 60% and uncertainty was reduced by more than 50%. Our results also suggest that policy responsiveness could have diminishing returns: improvements in the policy's accuracy and certainty tended to become smaller while policy costs per capita tended to become larger as design responsiveness increased. Finally, simulation results show how country-specific attributes influence the diffusion pattern of the technology. These findings have major implications for the design of future deployment policies supporting the diffusion of clean energy technologies. [ABSTRACT FROM AUTHOR]

• Small test setups for both pure and hybrid solar chimney system are built. • A numerical simulation method for the hybrid system is proposed and validated. • Hybrid systems greatly increase the total power output. • Hybrid systems can reduce land requirement of the solar-assisted air cleaning system. Under the background of global energy shortage and environment deterioration, researchers of the world pay high attention to develop renewable energy. This paper proposes a hybrid solar chimney and photovoltaic system for the novel solar-assisted air cleaning system. Small-scale laboratory setups are designed and fabricated. Experimental results reveal that replacing 50.60% acrylic glass of the collector top with photovoltaic panels will reduce the thermal air flow rate only by 14%, but generate significant electric power output. A three-dimensional numerical simulation model is established and validated by experimental results (air flow rate, temperature distribution). The model includes solar ray tracing model, surface to surface radiation model, buoyancy-driven flow and heat transfer model and power generation model. Then, this model is used to predict a large-scale system based on the Manzanares pilot power plant in Spain. For the large-scale system, the electrical energy generated by the photovoltaic panels can be used to drive suction fans to increase air input. Covering the entire top surface of the collector by photovoltaic panels (113-meter-wide), the total air flow rate would increase to 2.21 times compared with the system without photovoltaic panels. And setting photovoltaic panels on the collector bottom with 113-meter-wide, the total air flow rate would increase to 2.42 times. Thus, adding photovoltaic panels for the collector can greatly improve the utilization of solar energy. It can increase the amount of air purification, or reduce land requirement for the same flow rate. [ABSTRACT FROM AUTHOR]